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Zhou Y, Zhang Y, Zhao D, Yu X, Shen X, Zhou Y, Wang S, Qiu Y, Chen Y, Zhu F. TTD: Therapeutic Target Database describing target druggability information. Nucleic Acids Res 2024; 52:D1465-D1477. [PMID: 37713619 PMCID: PMC10767903 DOI: 10.1093/nar/gkad751] [Citation(s) in RCA: 56] [Impact Index Per Article: 56.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 07/31/2023] [Accepted: 09/05/2023] [Indexed: 09/17/2023] Open
Abstract
Target discovery is one of the essential steps in modern drug development, and the identification of promising targets is fundamental for developing first-in-class drug. A variety of methods have emerged for target assessment based on druggability analysis, which refers to the likelihood of a target being effectively modulated by drug-like agents. In the therapeutic target database (TTD), nine categories of established druggability characteristics were thus collected for 426 successful, 1014 clinical trial, 212 preclinical/patented, and 1479 literature-reported targets via systematic review. These characteristic categories were classified into three distinct perspectives: molecular interaction/regulation, human system profile and cell-based expression variation. With the rapid progression of technology and concerted effort in drug discovery, TTD and other databases were highly expected to facilitate the explorations of druggability characteristics for the discovery and validation of innovative drug target. TTD is now freely accessible at: https://idrblab.org/ttd/.
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Affiliation(s)
- Ying Zhou
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310058, China
- National Key Laboratory of Diagnosis and Treatment of Severe Infectious Disease, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital, Zhejiang University, Hangzhou, Zhejiang 310000, China
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
| | - Yintao Zhang
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Donghai Zhao
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Xinyuan Yu
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Xinyi Shen
- Department of Environmental Health Sciences, Yale School of Public Health, Yale University, New Haven 06510, USA
| | - Yuan Zhou
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Shanshan Wang
- Qian Xuesen Collaborative Research Center of Astrochemistry and Space Life Sciences, Institute of Drug Discovery Technology, Ningbo University, Ningbo 315211, China
| | - Yunqing Qiu
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310058, China
- National Key Laboratory of Diagnosis and Treatment of Severe Infectious Disease, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital, Zhejiang University, Hangzhou, Zhejiang 310000, China
| | - Yuzong Chen
- State Key Laboratory of Chemical Oncogenomics, Key Laboratory of Chemical Biology, The Graduate School at Shenzhen, Tsinghua University, Shenzhen 518055, China
- Institute of Biomedical Health Technology and Engineering, Shenzhen Bay Laboratory, Shenzhen 518000, China
| | - Feng Zhu
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310058, China
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
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2
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Morin A, Chu ECP, Sharma A, Adrian-Hamazaki A, Pavlidis P. Characterizing the targets of transcription regulators by aggregating ChIP-seq and perturbation expression data sets. Genome Res 2023; 33:763-778. [PMID: 37308292 PMCID: PMC10317128 DOI: 10.1101/gr.277273.122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Accepted: 04/26/2023] [Indexed: 06/14/2023]
Abstract
Mapping the gene targets of chromatin-associated transcription regulators (TRs) is a major goal of genomics research. ChIP-seq of TRs and experiments that perturb a TR and measure the differential abundance of gene transcripts are a primary means by which direct relationships are tested on a genomic scale. It has been reported that there is a poor overlap in the evidence across gene regulation strategies, emphasizing the need for integrating results from multiple experiments. Although research consortia interested in gene regulation have produced a valuable trove of high-quality data, there is an even greater volume of TR-specific data throughout the literature. In this study, we show a workflow for the identification, uniform processing, and aggregation of ChIP-seq and TR perturbation experiments for the ultimate purpose of ranking human and mouse TR-target interactions. Focusing on an initial set of eight regulators (ASCL1, HES1, MECP2, MEF2C, NEUROD1, PAX6, RUNX1, and TCF4), we identified 497 experiments suitable for analysis. We used this corpus to examine data concordance, to identify systematic patterns of the two data types, and to identify putative orthologous interactions between human and mouse. We build upon commonly used strategies to forward a procedure for aggregating and combining these two genomic methodologies, assessing these rankings against independent literature-curated evidence. Beyond a framework extensible to other TRs, our work also provides empirically ranked TR-target listings, as well as transparent experiment-level gene summaries for community use.
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Affiliation(s)
- Alexander Morin
- Michael Smith Laboratories, University of British Columbia, Vancouver, British Columbia V6T 1Z4, Canada
- Department of Psychiatry, University of British Columbia, Vancouver, British Columbia V6T 1Z4, Canada
- Graduate Program in Bioinformatics, University of British Columbia, Vancouver, British Columbia V6T 1Z4, Canada
| | - Eric Ching-Pan Chu
- Michael Smith Laboratories, University of British Columbia, Vancouver, British Columbia V6T 1Z4, Canada
- Department of Psychiatry, University of British Columbia, Vancouver, British Columbia V6T 1Z4, Canada
- Graduate Program in Bioinformatics, University of British Columbia, Vancouver, British Columbia V6T 1Z4, Canada
| | - Aman Sharma
- Michael Smith Laboratories, University of British Columbia, Vancouver, British Columbia V6T 1Z4, Canada
| | - Alex Adrian-Hamazaki
- Michael Smith Laboratories, University of British Columbia, Vancouver, British Columbia V6T 1Z4, Canada
- Department of Psychiatry, University of British Columbia, Vancouver, British Columbia V6T 1Z4, Canada
- Graduate Program in Bioinformatics, University of British Columbia, Vancouver, British Columbia V6T 1Z4, Canada
| | - Paul Pavlidis
- Michael Smith Laboratories, University of British Columbia, Vancouver, British Columbia V6T 1Z4, Canada;
- Department of Psychiatry, University of British Columbia, Vancouver, British Columbia V6T 1Z4, Canada
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3
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He H, Duo H, Hao Y, Zhang X, Zhou X, Zeng Y, Li Y, Li B. Computational drug repurposing by exploiting large-scale gene expression data: Strategy, methods and applications. Comput Biol Med 2023; 155:106671. [PMID: 36805225 DOI: 10.1016/j.compbiomed.2023.106671] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Revised: 02/05/2023] [Accepted: 02/10/2023] [Indexed: 02/18/2023]
Abstract
De novo drug development is an extremely complex, time-consuming and costly task. Urgent needs for therapies of various diseases have greatly accelerated searches for more effective drug development methods. Luckily, drug repurposing provides a new and effective perspective on disease treatment. Rapidly increased large-scale transcriptome data paints a detailed prospect of gene expression during disease onset and thus has received wide attention in the field of computational drug repurposing. However, how to efficiently mine transcriptome data and identify new indications for old drugs remains a critical challenge. This review discussed the irreplaceable role of transcriptome data in computational drug repurposing and summarized some representative databases, tools and strategies. More importantly, it proposed a practical guideline through establishing the correspondence between three gene expression data types and five strategies, which would facilitate researchers to adopt appropriate strategies to deeply mine large-scale transcriptome data and discover more effective therapies.
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Affiliation(s)
- Hao He
- College of Life Sciences, Chongqing Normal University, Chongqing, 400044, PR China; State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Institutes of Brain Science, Fudan University, Shanghai, 200032, PR China
| | - Hongrui Duo
- College of Life Sciences, Chongqing Normal University, Chongqing, 400044, PR China
| | - Youjin Hao
- College of Life Sciences, Chongqing Normal University, Chongqing, 400044, PR China
| | - Xiaoxi Zhang
- College of Life Sciences, Chongqing Normal University, Chongqing, 400044, PR China
| | - Xinyi Zhou
- College of Life Sciences, Chongqing Normal University, Chongqing, 400044, PR China
| | - Yujie Zeng
- College of Life Sciences, Chongqing Normal University, Chongqing, 400044, PR China
| | - Yinghong Li
- The Key Laboratory on Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, Chongqing, 400065, PR China
| | - Bo Li
- College of Life Sciences, Chongqing Normal University, Chongqing, 400044, PR China.
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4
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Guo S, Xu Z, Dong X, Hu D, Jiang Y, Wang Q, Zhang J, Zhou Q, Liu S, Song W. GPSAdb: a comprehensive web resource for interactive exploration of genetic perturbation RNA-seq datasets. Nucleic Acids Res 2022; 51:D964-D968. [PMID: 36416261 PMCID: PMC9825484 DOI: 10.1093/nar/gkac1066] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Revised: 10/21/2022] [Accepted: 10/26/2022] [Indexed: 11/24/2022] Open
Abstract
Gene knock-out/down methods are commonly used to explore the functions of genes of interest, but a database that systematically collects perturbed data is not available currently. Manual curation of all the available human cell line perturbed RNA-seq datasets enabled us to develop a comprehensive human perturbation database (GPSAdb, https://www.gpsadb.com/). The current version of GPSAdb collected 3048 RNA-seq datasets associated with 1458 genes, which were knocked out/down by siRNA, shRNA, CRISPR/Cas9, or CRISPRi. The database provides full exploration of these datasets and generated 6096 new perturbed gene sets (up and down separately). GPSAdb integrated the gene sets and developed an online tool, genetic perturbation similarity analysis (GPSA), to identify candidate causal perturbations from differential gene expression data. In summary, GPSAdb is a powerful platform that aims to assist life science researchers to easily access and analyze public perturbed data and explore differential gene expression data in depth.
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Affiliation(s)
- Shipeng Guo
- To whom correspondence should be addressed. Tel: +86 23 63637851;
| | - Zhougeng Xu
- National Key Laboratory of Plant Molecular Genetics (NKLPMG), CAS Center for Excellence in Molecular Plant Sciences (CEMPS), Institute of Plant Physiology and Ecology (SIPPE), Chinese Academy of Sciences (CAS), Shanghai, China
| | - Xiangjun Dong
- Chongqing Key Laboratory of Translational Medical Research in Cognitive Development and Learning and Memory Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, National Clinical Research Center for Child Health and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Children's Hospital of Chongqing Medical University, Chongqing, China
| | - Dongjie Hu
- Chongqing Key Laboratory of Translational Medical Research in Cognitive Development and Learning and Memory Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, National Clinical Research Center for Child Health and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Children's Hospital of Chongqing Medical University, Chongqing, China
| | - Yanshuang Jiang
- Chongqing Key Laboratory of Translational Medical Research in Cognitive Development and Learning and Memory Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, National Clinical Research Center for Child Health and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Children's Hospital of Chongqing Medical University, Chongqing, China
| | - Qunxian Wang
- Chongqing Key Laboratory of Translational Medical Research in Cognitive Development and Learning and Memory Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, National Clinical Research Center for Child Health and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Children's Hospital of Chongqing Medical University, Chongqing, China
| | - Jie Zhang
- Chongqing Key Laboratory of Translational Medical Research in Cognitive Development and Learning and Memory Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, National Clinical Research Center for Child Health and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Children's Hospital of Chongqing Medical University, Chongqing, China
| | - Qian Zhou
- Chongqing Key Laboratory of Translational Medical Research in Cognitive Development and Learning and Memory Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, National Clinical Research Center for Child Health and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Children's Hospital of Chongqing Medical University, Chongqing, China
| | - Shengchun Liu
- Correspondence may also be addressed to Shengchun Liu.
| | - Weihong Song
- Correspondence may also be addressed to Weihong Song.
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5
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Zhai Z, Zhang X, Zhou L, Lin Z, Kuang N, Li Q, Ma Q, Tao H, Gao J, Ma S, Pan J. PertOrg 1.0: a comprehensive resource of multilevel alterations induced in model organisms by in vivo genetic perturbation. Nucleic Acids Res 2022; 51:D1094-D1101. [PMID: 36243973 PMCID: PMC9825601 DOI: 10.1093/nar/gkac872] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Revised: 09/04/2022] [Accepted: 09/28/2022] [Indexed: 01/30/2023] Open
Abstract
Genetically modified organisms (GMOs) can be generated to model human genetic disease or plant disease resistance, and they have contributed to the exploration and understanding of gene function, physiology, disease onset and drug target discovery. Here, PertOrg (http://www.inbirg.com/pertorg/) was introduced to provide multilevel alterations in GMOs. Raw data of 58 707 transcriptome profiles and associated information, such as phenotypic alterations, were collected and curated from studies involving in vivo genetic perturbation (e.g. knockdown, knockout and overexpression) in eight model organisms, including mouse, rat and zebrafish. The transcriptome profiles from before and after perturbation were organized into 10 116 comparison datasets, including 122 single-cell RNA-seq datasets. The raw data were checked and analysed using widely accepted and standardized pipelines to identify differentially expressed genes (DEGs) in perturbed organisms. As a result, 8 644 148 DEGs were identified and deposited as signatures of gene perturbations. Downstream functional enrichment analysis, cell type analysis and phenotypic alterations were also provided when available. Multiple search methods and analytical tools were created and implemented. Furthermore, case studies were presented to demonstrate how users can utilize the database. PertOrg 1.0 will be a valuable resource aiding in the exploration of gene functions, biological processes and disease models.
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Affiliation(s)
| | | | | | - Zhewei Lin
- Center for Novel Target and Therapeutic Intervention, Institute of Life Sciences, Chongqing Medical University, Chongqing 400016, China
| | - Ni Kuang
- Center for Novel Target and Therapeutic Intervention, Institute of Life Sciences, Chongqing Medical University, Chongqing 400016, China
| | - Qiang Li
- Center for Novel Target and Therapeutic Intervention, Institute of Life Sciences, Chongqing Medical University, Chongqing 400016, China
| | - Qinfeng Ma
- Center for Novel Target and Therapeutic Intervention, Institute of Life Sciences, Chongqing Medical University, Chongqing 400016, China
| | - Haodong Tao
- Center for Novel Target and Therapeutic Intervention, Institute of Life Sciences, Chongqing Medical University, Chongqing 400016, China
| | - Jieya Gao
- Center for Novel Target and Therapeutic Intervention, Institute of Life Sciences, Chongqing Medical University, Chongqing 400016, China
| | - Shiyong Ma
- Center for Novel Target and Therapeutic Intervention, Institute of Life Sciences, Chongqing Medical University, Chongqing 400016, China
| | - Jianbo Pan
- To whom correspondence should be addressed. Tel: +86 23 684 80209; Fax: +86 23 684 80209;
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6
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Naitore C, Villinger J, Kibet CK, Kalayou S, Bargul JL, Christoffels A, Masiga DK. The developmentally dynamic microRNA transcriptome of Glossina pallidipes tsetse flies, vectors of animal trypanosomiasis. BIOINFORMATICS ADVANCES 2021; 2:vbab047. [PMID: 36699416 PMCID: PMC9710702 DOI: 10.1093/bioadv/vbab047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Revised: 11/25/2021] [Accepted: 12/24/2021] [Indexed: 01/28/2023]
Abstract
Summary MicroRNAs (miRNAs) are single stranded gene regulators of 18-25 bp in length. They play a crucial role in regulating several biological processes in insects. However, the functions of miRNA in Glossina pallidipes, one of the biological vectors of African animal trypanosomosis in sub-Saharan Africa, remain poorly characterized. We used a combination of both molecular biology and bioinformatics techniques to identify miRNA genes at different developmental stages (larvae, pupae, teneral and reproductive unmated adults, gravid females) and sexes of G. pallidipes. We identified 157 mature miRNA genes, including 12 novel miRNAs unique to G. pallidipes. Moreover, we identified 93 miRNA genes that were differentially expressed by sex and/or in specific developmental stages. By combining both miRanda and RNAhybrid algorithms, we identified 5550 of their target genes. Further analyses with the Gene Ontology term and KEGG pathways for these predicted target genes suggested that the miRNAs may be involved in key developmental biological processes. Our results provide the first repository of G. pallidipes miRNAs across developmental stages, some of which appear to play crucial roles in tsetse fly development. Hence, our findings provide a better understanding of tsetse biology and a baseline for exploring miRNA genes in tsetse flies. Availability and implementation Raw sequence data are available from NCBI Sequence Read Archives (SRA) under Bioproject accession number PRJNA590626. Supplementary information Supplementary data are available at Bioinformatics Advances online.
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Affiliation(s)
- Careen Naitore
- International Centre of Insect Physiology and Ecology (icipe), P.O. Box 30772, Nairobi 00100, Kenya,Department of Biochemistry, Jomo Kenyatta University of Agriculture and Technology (JKUAT), P.O. Box 62000, Nairobi 00200, Kenya
| | - Jandouwe Villinger
- International Centre of Insect Physiology and Ecology (icipe), P.O. Box 30772, Nairobi 00100, Kenya,To whom correspondence should be addressed. or
| | - Caleb K Kibet
- International Centre of Insect Physiology and Ecology (icipe), P.O. Box 30772, Nairobi 00100, Kenya
| | - Shewit Kalayou
- International Centre of Insect Physiology and Ecology (icipe), P.O. Box 30772, Nairobi 00100, Kenya
| | - Joel L Bargul
- International Centre of Insect Physiology and Ecology (icipe), P.O. Box 30772, Nairobi 00100, Kenya,Department of Biochemistry, Jomo Kenyatta University of Agriculture and Technology (JKUAT), P.O. Box 62000, Nairobi 00200, Kenya
| | - Alan Christoffels
- South African Medical Research Council Bioinformatics Unit, South African National Bioinformatics Institute (SANBI), University of the Western Cape, Bellville 7530, South Africa
| | - Daniel K Masiga
- International Centre of Insect Physiology and Ecology (icipe), P.O. Box 30772, Nairobi 00100, Kenya,To whom correspondence should be addressed. or
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7
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Ji Y, Lotfollahi M, Wolf FA, Theis FJ. Machine learning for perturbational single-cell omics. Cell Syst 2021; 12:522-537. [PMID: 34139164 DOI: 10.1016/j.cels.2021.05.016] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Revised: 05/04/2021] [Accepted: 05/19/2021] [Indexed: 12/18/2022]
Abstract
Cell biology is fundamentally limited in its ability to collect complete data on cellular phenotypes and the wide range of responses to perturbation. Areas such as computer vision and speech recognition have addressed this problem of characterizing unseen or unlabeled conditions with the combined advances of big data, deep learning, and computing resources in the past 5 years. Similarly, recent advances in machine learning approaches enabled by single-cell data start to address prediction tasks in perturbation response modeling. We first define objectives in learning perturbation response in single-cell omics; survey existing approaches, resources, and datasets (https://github.com/theislab/sc-pert); and discuss how a perturbation atlas can enable deep learning models to construct an informative perturbation latent space. We then examine future avenues toward more powerful and explainable modeling using deep neural networks, which enable the integration of disparate information sources and an understanding of heterogeneous, complex, and unseen systems.
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Affiliation(s)
- Yuge Ji
- Institute of Computational Biology, Helmholtz Center Munich, Munich, Germany; Department of Mathematics, Technical University of Munich, Munich, Germany
| | - Mohammad Lotfollahi
- Institute of Computational Biology, Helmholtz Center Munich, Munich, Germany; TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany
| | - F Alexander Wolf
- Institute of Computational Biology, Helmholtz Center Munich, Munich, Germany; Cellarity, Cambridge, MA, USA
| | - Fabian J Theis
- Institute of Computational Biology, Helmholtz Center Munich, Munich, Germany; Department of Mathematics, Technical University of Munich, Munich, Germany; Cellarity, Cambridge, MA, USA.
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8
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Lim N, Tesar S, Belmadani M, Poirier-Morency G, Mancarci BO, Sicherman J, Jacobson M, Leong J, Tan P, Pavlidis P. Curation of over 10 000 transcriptomic studies to enable data reuse. Database (Oxford) 2021; 2021:6143045. [PMID: 33599246 PMCID: PMC7904053 DOI: 10.1093/database/baab006] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2020] [Revised: 12/09/2020] [Accepted: 01/28/2021] [Indexed: 01/07/2023]
Abstract
Vast amounts of transcriptomic data reside in public repositories, but effective reuse remains challenging. Issues include unstructured dataset metadata, inconsistent data processing and quality control, and inconsistent probe-gene mappings across microarray technologies. Thus, extensive curation and data reprocessing are necessary prior to any reuse. The Gemma bioinformatics system was created to help address these issues. Gemma consists of a database of curated transcriptomic datasets, analytical software, a web interface and web services. Here we present an update on Gemma's holdings, data processing and analysis pipelines, our curation guidelines, and software features. As of June 2020, Gemma contains 10 811 manually curated datasets (primarily human, mouse and rat), over 395 000 samples and hundreds of curated transcriptomic platforms (both microarray and RNA sequencing). Dataset topics were represented with 10 215 distinct terms from 12 ontologies, for a total of 54 316 topic annotations (mean topics/dataset = 5.2). While Gemma has broad coverage of conditions and tissues, it captures a large majority of available brain-related datasets, accounting for 34% of its holdings. Users can access the curated data and differential expression analyses through the Gemma website, RESTful service and an R package. Database URL: https://gemma.msl.ubc.ca/home.html.
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Affiliation(s)
- Nathaniel Lim
- Genome Science and Technology Graduate Program, University of British Columbia, Vancouver, BC V6T1Z4, Canada,Michael Smith Laboratories, University of British Columbia, 2185 East Mall, Vancouver, BC V6T1Z4, Canada
| | - Stepan Tesar
- Michael Smith Laboratories, University of British Columbia, 2185 East Mall, Vancouver, BC V6T1Z4, Canada
| | - Manuel Belmadani
- Michael Smith Laboratories, University of British Columbia, 2185 East Mall, Vancouver, BC V6T1Z4, Canada
| | - Guillaume Poirier-Morency
- Michael Smith Laboratories, University of British Columbia, 2185 East Mall, Vancouver, BC V6T1Z4, Canada
| | - Burak Ogan Mancarci
- Michael Smith Laboratories, University of British Columbia, 2185 East Mall, Vancouver, BC V6T1Z4, Canada,Bioinformatics Graduate Program, University of British Columbia, Vancouver, BC V6T1Z4, Canada
| | - Jordan Sicherman
- Michael Smith Laboratories, University of British Columbia, 2185 East Mall, Vancouver, BC V6T1Z4, Canada,Bioinformatics Graduate Program, University of British Columbia, Vancouver, BC V6T1Z4, Canada
| | - Matthew Jacobson
- Michael Smith Laboratories, University of British Columbia, 2185 East Mall, Vancouver, BC V6T1Z4, Canada
| | - Justin Leong
- Michael Smith Laboratories, University of British Columbia, 2185 East Mall, Vancouver, BC V6T1Z4, Canada
| | - Patrick Tan
- Michael Smith Laboratories, University of British Columbia, 2185 East Mall, Vancouver, BC V6T1Z4, Canada
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9
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Chandradoss KR, Chawla B, Dhuppar S, Nayak R, Ramachandran R, Kurukuti S, Mazumder A, Sandhu KS. CTCF-Mediated Genome Architecture Regulates the Dosage of Mitotically Stable Mono-allelic Expression of Autosomal Genes. Cell Rep 2020; 33:108302. [PMID: 33113374 DOI: 10.1016/j.celrep.2020.108302] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2017] [Revised: 07/31/2020] [Accepted: 09/30/2020] [Indexed: 11/30/2022] Open
Abstract
The mechanisms that guide the clonally stable random mono-allelic expression of autosomal genes remain enigmatic. We show that (1) mono-allelically expressed (MAE) genes are assorted and insulated from bi-allelically expressed (BAE) genes through CTCF-mediated chromatin loops; (2) the cell-type-specific dynamics of mono-allelic expression coincides with the gain and loss of chromatin insulator sites; (3) dosage of MAE genes is more sensitive to the loss of chromatin insulation than that of BAE genes; and (4) inactive alleles of MAE genes are significantly more insulated than active alleles and are de-repressed upon CTCF depletion. This alludes to a topology wherein the inactive alleles of MAE genes are insulated from the spatial interference of transcriptional states from the neighboring bi-allelic domains via CTCF-mediated loops. We propose that CTCF functions as a typical insulator on inactive alleles, but facilitates transcription through enhancer-linking on active allele of MAE genes, indicating widespread allele-specific regulatory roles of CTCF.
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Affiliation(s)
- Keerthivasan Raanin Chandradoss
- Department of Biological Sciences, Indian Institute of Science Education and Research (IISER)-Mohali, Knowledge City, Sector 81, SAS Nagar 140306, India
| | - Bindia Chawla
- Department of Biological Sciences, Indian Institute of Science Education and Research (IISER)-Mohali, Knowledge City, Sector 81, SAS Nagar 140306, India
| | - Shivnarayan Dhuppar
- TIFR Centre for Interdisciplinary Sciences, Tata Institute of Fundamental Research (TIFR) Hyderabad, 36/P, Gopanpally Village, Serilingampally Mandal, Hyderabad 500046, India
| | - Rakhee Nayak
- Department of Animal Biology, School of Life Sciences, University of Hyderabad, Prof. C.R. Rao Road, Gachibowli, Hyderabad 500046, India
| | - Rajesh Ramachandran
- Department of Biological Sciences, Indian Institute of Science Education and Research (IISER)-Mohali, Knowledge City, Sector 81, SAS Nagar 140306, India
| | - Sreenivasulu Kurukuti
- Department of Animal Biology, School of Life Sciences, University of Hyderabad, Prof. C.R. Rao Road, Gachibowli, Hyderabad 500046, India
| | - Aprotim Mazumder
- TIFR Centre for Interdisciplinary Sciences, Tata Institute of Fundamental Research (TIFR) Hyderabad, 36/P, Gopanpally Village, Serilingampally Mandal, Hyderabad 500046, India
| | - Kuljeet Singh Sandhu
- Department of Biological Sciences, Indian Institute of Science Education and Research (IISER)-Mohali, Knowledge City, Sector 81, SAS Nagar 140306, India.
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10
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Keenan AB, Wojciechowicz ML, Wang Z, Jagodnik KM, Jenkins SL, Lachmann A, Ma'ayan A. Connectivity Mapping: Methods and Applications. Annu Rev Biomed Data Sci 2019. [DOI: 10.1146/annurev-biodatasci-072018-021211] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Connectivity mapping resources consist of signatures representing changes in cellular state following systematic small-molecule, disease, gene, or other form of perturbations. Such resources enable the characterization of signatures from novel perturbations based on similarity; provide a global view of the space of many themed perturbations; and allow the ability to predict cellular, tissue, and organismal phenotypes for perturbagens. A signature search engine enables hypothesis generation by finding connections between query signatures and the database of signatures. This framework has been used to identify connections between small molecules and their targets, to discover cell-specific responses to perturbations and ways to reverse disease expression states with small molecules, and to predict small-molecule mimickers for existing drugs. This review provides a historical perspective and the current state of connectivity mapping resources with a focus on both methodology and community implementations.
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Affiliation(s)
- Alexandra B. Keenan
- Department of Pharmacological Sciences and Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Megan L. Wojciechowicz
- Department of Pharmacological Sciences and Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Zichen Wang
- Department of Pharmacological Sciences and Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Kathleen M. Jagodnik
- Department of Pharmacological Sciences and Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Sherry L. Jenkins
- Department of Pharmacological Sciences and Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Alexander Lachmann
- Department of Pharmacological Sciences and Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Avi Ma'ayan
- Department of Pharmacological Sciences and Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
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11
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Djordjevic D, Tang JYS, Chen YX, Kwan SLS, Ling RWK, Qian G, Woo CYY, Ellis SJ, Ho JWK. Discovery of perturbation gene targets via free text metadata mining in Gene Expression Omnibus. Comput Biol Chem 2019; 80:152-158. [PMID: 30959271 DOI: 10.1016/j.compbiolchem.2019.03.014] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2019] [Accepted: 03/23/2019] [Indexed: 10/27/2022]
Abstract
There exists over 2.5 million publicly available gene expression samples across 101,000 data series in NCBI's Gene Expression Omnibus (GEO) database. Due to the lack of the use of standardised ontology terms in GEO's free text metadata to annotate the experimental type and sample type, this database remains difficult to harness computationally without significant manual intervention. In this work, we present an interactive R/Shiny tool called GEOracle that utilises text mining and machine learning techniques to automatically identify perturbation experiments, group treatment and control samples and perform differential expression. We present applications of GEOracle to discover conserved signalling pathway target genes and identify an organ specific gene regulatory network. GEOracle is effective in discovering perturbation gene targets in GEO by harnessing its free text metadata. Its effectiveness and applicability has been demonstrated by cross validation and two real-life case studies. It opens up new avenues to unlock the gene regulatory information embedded inside large biological databases such as GEO. GEOracle is available at https://github.com/VCCRI/GEOracle.
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Affiliation(s)
- Djordje Djordjevic
- Victor Chang Cardiac Research Institute, Sydney, Australia; University of New South Wales, Sydney, Australia
| | - Joshua Y S Tang
- Victor Chang Cardiac Research Institute, Sydney, Australia; University of New South Wales, Sydney, Australia
| | - Yun Xin Chen
- Victor Chang Cardiac Research Institute, Sydney, Australia
| | | | | | - Gordon Qian
- Victor Chang Cardiac Research Institute, Sydney, Australia
| | | | - Samuel J Ellis
- Victor Chang Cardiac Research Institute, Sydney, Australia
| | - Joshua W K Ho
- Victor Chang Cardiac Research Institute, Sydney, Australia; University of New South Wales, Sydney, Australia; School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
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12
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Paik YK, Lane L, Kawamura T, Chen YJ, Cho JY, LaBaer J, Yoo JS, Domont G, Corrales F, Omenn GS, Archakov A, Encarnación-Guevara S, Lui S, Salekdeh GH, Cho JY, Kim CY, Overall CM. Launching the C-HPP neXt-CP50 Pilot Project for Functional Characterization of Identified Proteins with No Known Function. J Proteome Res 2018; 17:4042-4050. [PMID: 30269496 PMCID: PMC6693327 DOI: 10.1021/acs.jproteome.8b00383] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
An important goal of the Human Proteome Organization (HUPO) Chromosome-centric Human Proteome Project (C-HPP) is to correctly define the number of canonical proteins encoded by their cognate open reading frames on each chromosome in the human genome. When identified with high confidence of protein evidence (PE), such proteins are termed PE1 proteins in the online database resource, neXtProt. However, proteins that have not been identified unequivocally at the protein level but that have other evidence suggestive of their existence (PE2-4) are termed missing proteins (MPs). The number of MPs has been reduced from 5511 in 2012 to 2186 in 2018 (neXtProt 2018-01-17 release). Although the annotation of the human proteome has made significant progress, the "parts list" alone does not inform function. Indeed, 1937 proteins representing ∼10% of the human proteome have no function either annotated from experimental characterization or predicted by homology to other proteins. Specifically, these 1937 "dark proteins" of the so-called dark proteome are composed of 1260 functionally uncharacterized but identified PE1 proteins, designated as uPE1, plus 677 MPs from categories PE2-PE4, which also have no known or predicted function and are termed uMPs. At the HUPO-2017 Annual Meeting, the C-HPP officially adopted the uPE1 pilot initiative, with 14 participating international teams later committing to demonstrate the feasibility of the functional characterization of large numbers of dark proteins (CP), starting first with 50 uPE1 proteins, in a stepwise chromosome-centric organizational manner. The second aim of the feasibility phase to characterize protein (CP) functions of 50 uPE1 proteins, termed the neXt-CP50 initiative, is to utilize a variety of approaches and workflows according to individual team expertise, interest, and resources so as to enable the C-HPP to recommend experimentally proven workflows to the proteome community within 3 years. The results from this pilot will not only be the cornerstone of a larger characterization initiative but also enhance understanding of the human proteome and integrated cellular networks for the discovery of new mechanisms of pathology, mechanistically informative biomarkers, and rational drug targets.
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Affiliation(s)
- Young-Ki Paik
- Yonsei Proteome Research Center and Department of Integrative Omics, Yonsei University, Sudaemoon-ku, Seoul, Korea
| | - Lydie Lane
- CALIPHO group, Swiss Institute of Bioinformatics & Department of Microbiology and Molecular medicine, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Takeshi Kawamura
- Proteomics Laboratory, Isotope Science Center, The University of Tokyo, Bunkyo-Ku, Tokyo 113-0032 Japan
| | - Yu-Ju Chen
- Institute of Chemistry Academia Sinica, 128 Academia Road Sec. 2, Nankang Taipei 115 Taiwan
| | - Je-Yoel Cho
- Research Institute for Veterinary Science, College of Veterinary Medicine, Seoul University, 1 Gwanak-, Gwanak-gu, 151-742 Seoul, South Korea
| | - Joshua LaBaer
- McAllister Ave. Arizona State University, Tempe, Arizona, 85287-5001, USA
| | - Jong Shin Yoo
- Division of Mass Spectrometry Research, Korea Basic Science Institute, Ochang, Korea
| | - Gilberto Domont
- Federal University of Rio de Janeiro Institute of Chemistry, Rio de Janeiro, RJ Brazil
| | - Fernando Corrales
- Functional Proteomics Laboratory National Center of Biotechnology, CSIC 28049 Madrid, Spain
| | - Gilbert S. Omenn
- Center for Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan 48109-2218, United States
| | | | | | - Siqi Lui
- BGI-Shenzhen, Beishan Industrial Zone, Yantian District, Shenzhen, 518083, China
| | - Ghasem Hosseini Salekdeh
- Department of Molecular Systems Biology, Royan Institute for Stem Cell Biology and Technology, 1665659911, Tehran, Iran
- Department of Molecular Sciences, Macquarie University, Sydney, Australia
| | - Jin-Young Cho
- Yonsei Proteome Research Center and Department of Integrative Omics, Yonsei University, Sudaemoon-ku, Seoul, Korea
| | - Chae-Yeon Kim
- Yonsei Proteome Research Center and Department of Integrative Omics, Yonsei University, Sudaemoon-ku, Seoul, Korea
| | - Christopher M. Overall
- Centre for Blood Research, Departments of Oral Biological & Medical Sciences, and Biochemistry & Molecular Biology, Faculty of Dentistry, University of British Columbia, Vancouver, Canada
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Yu F, Zhang G, Shi A, Hu J, Li F, Zhang X, Zhang Y, Huang J, Xiao Y, Li X, Cheng S. LnChrom: a resource of experimentally validated lncRNA-chromatin interactions in human and mouse. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2018; 2018:4999394. [PMID: 29788225 PMCID: PMC5960994 DOI: 10.1093/database/bay039] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/07/2017] [Accepted: 03/23/2018] [Indexed: 12/14/2022]
Abstract
Long non-coding RNAs (lncRNAs) constitute an important layer of chromatin regulation that contributes to various biological processes and diseases. By interacting with chromatin, many lncRNAs can regulate that state of chromatin by recruiting chromatin-modifying complexes and thus control large-scale gene expression programs. However, the available information on interactions between lncRNAs and chromatin is hidden in a large amount of dispersed literature and has not been extensively collected. We established the LnChrom database, a manually curated resource of experimentally validated lncRNA–chromatin interactions. The current release of LnChrom includes 382 743 interactions in human and mouse. We also manually collected detailed metadata for each interaction pair, including those of chromatin modifying factors, epigenetic marks and disease associations. LnChrom provides a user-friendly interface to facilitate browsing, searching and retrieving of lncRNA–chromatin interaction data. Additionally, a large amount of multi-omics data was integrated into LnChrom to aid in characterizing the effects of lncRNA–chromatin interactions on epigenetic modifications and transcriptional expression. We believe that LnChrom is a timely and valuable resource that can greatly motivate mechanistic research into lncRNAs. Database URL: http://biocc.hrbmu.edu.cn/LnChrom/
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Affiliation(s)
- Fulong Yu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China
| | - Guanxiong Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China
| | - Aiai Shi
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China
| | - Jing Hu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China
| | - Feng Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China
| | - Xinxin Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China
| | - Yan Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China
| | - Jian Huang
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Yun Xiao
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China
| | - Xia Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China
| | - Shujun Cheng
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China.,State Key Laboratory of Molecular Oncology, Department of Etiology and Carcinogenesis, Cancer Institute and Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100021, China
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14
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Xu J, Shi A, Long Z, Xu L, Liao G, Deng C, Yan M, Xie A, Luo T, Huang J, Xiao Y, Li X. Capturing functional long non-coding RNAs through integrating large-scale causal relations from gene perturbation experiments. EBioMedicine 2018; 35:369-380. [PMID: 30177244 PMCID: PMC6156711 DOI: 10.1016/j.ebiom.2018.08.050] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2018] [Revised: 08/15/2018] [Accepted: 08/22/2018] [Indexed: 02/06/2023] Open
Abstract
Characterizing functions of long noncoding RNAs (lncRNAs) remains a major challenge, mostly due to the lack of lncRNA-involved regulatory relationships. A wide array of genome-wide expression profiles generated by gene perturbation have been widely used to capture causal links between perturbed genes and response genes. Through annotating >600 gene perturbation profiles, over 354,000 causal relationships between perturbed genes and lncRNAs were identified. This large-scale resource of causal relations inspired us to develop a novel computational approach LnCAR for inferring lncRNAs' functions, which showed a higher accuracy than the co-expression based approach. By application of LnCAR to the cancer hallmark processes, we identified 38 lncRNAs involved in distinct carcinogenic processes. The "activating invasion & metastasis" related lncRNAs were strongly associated with metastatic progression in various cancer types and could act as a predictor of cancer metastasis. Meanwhile, the "evading immune destruction" related lncRNAs showed significant associations with immune infiltration of various immune cells and, importantly, can predict response to anti-PD-1 immunotherapy, suggesting their potential roles as biomarkers for immune therapy. Taken together, our approach provides a novel way to systematically reveal functions of lncRNAs, which will be helpful for further experimental exploration and clinical translational research of lncRNAs.
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Affiliation(s)
- Jinyuan Xu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China
| | - Aiai Shi
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China
| | - Zhilin Long
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China
| | - Liwen Xu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China
| | - Gaoming Liao
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China
| | - Chunyu Deng
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China
| | - Min Yan
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China
| | - Aiming Xie
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China
| | - Tao Luo
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China
| | - Jian Huang
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 611731, China.
| | - Yun Xiao
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China; Key Laboratory of Cardiovascular Medicine Research, Harbin Medical University, Harbin, Heilongjiang 150086, China.
| | - Xia Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China; Key Laboratory of Cardiovascular Medicine Research, Harbin Medical University, Harbin, Heilongjiang 150086, China.
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15
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Cieślik M, Chinnaiyan AM. Cancer transcriptome profiling at the juncture of clinical translation. Nat Rev Genet 2017; 19:93-109. [PMID: 29279605 DOI: 10.1038/nrg.2017.96] [Citation(s) in RCA: 156] [Impact Index Per Article: 22.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Methodological breakthroughs over the past four decades have repeatedly revolutionized transcriptome profiling. Using RNA sequencing (RNA-seq), it has now become possible to sequence and quantify the transcriptional outputs of individual cells or thousands of samples. These transcriptomes provide a link between cellular phenotypes and their molecular underpinnings, such as mutations. In the context of cancer, this link represents an opportunity to dissect the complexity and heterogeneity of tumours and to discover new biomarkers or therapeutic strategies. Here, we review the rationale, methodology and translational impact of transcriptome profiling in cancer.
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Affiliation(s)
- Marcin Cieślik
- Michigan Center for Translational Pathology, University of Michigan.,Department of Pathology, University of Michigan
| | - Arul M Chinnaiyan
- Michigan Center for Translational Pathology, University of Michigan.,Department of Pathology, University of Michigan.,Comprehensive Cancer Center, University of Michigan.,Department of Urology, University of Michigan.,Howard Hughes Medical Institute, University of Michigan, Ann Arbor, Michigan 48109, USA
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16
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Mazaya M, Trinh HC, Kwon YK. Construction and analysis of gene-gene dynamics influence networks based on a Boolean model. BMC SYSTEMS BIOLOGY 2017; 11:133. [PMID: 29322926 PMCID: PMC5763298 DOI: 10.1186/s12918-017-0509-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
BACKGROUND Identification of novel gene-gene relations is a crucial issue to understand system-level biological phenomena. To this end, many methods based on a correlation analysis of gene expressions or structural analysis of molecular interaction networks have been proposed. They have a limitation in identifying more complicated gene-gene dynamical relations, though. RESULTS To overcome this limitation, we proposed a measure to quantify a gene-gene dynamical influence (GDI) using a Boolean network model and constructed a GDI network to indicate existence of a dynamical influence for every ordered pair of genes. It represents how much a state trajectory of a target gene is changed by a knockout mutation subject to a source gene in a gene-gene molecular interaction (GMI) network. Through a topological comparison between GDI and GMI networks, we observed that the former network is denser than the latter network, which implies that there exist many gene pairs of dynamically influencing but molecularly non-interacting relations. In addition, a larger number of hub genes were generated in the GDI network. On the other hand, there was a correlation between these networks such that the degree value of a node was positively correlated to each other. We further investigated the relationships of the GDI value with structural properties and found that there are negative and positive correlations with the length of a shortest path and the number of paths, respectively. In addition, a GDI network could predict a set of genes whose steady-state expression is affected in E. coli gene-knockout experiments. More interestingly, we found that the drug-targets with side-effects have a larger number of outgoing links than the other genes in the GDI network, which implies that they are more likely to influence the dynamics of other genes. Finally, we found biological evidences showing that the gene pairs which are not molecularly interacting but dynamically influential can be considered for novel gene-gene relationships. CONCLUSION Taken together, construction and analysis of the GDI network can be a useful approach to identify novel gene-gene relationships in terms of the dynamical influence.
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Affiliation(s)
- Maulida Mazaya
- Department of Electrical/Electronic and Computer Engineering, University of Ulsan, 93 Daehak-ro, Nam-gu, Ulsan, 44610 Republic of Korea
| | - Hung-Cuong Trinh
- Department of Electrical/Electronic and Computer Engineering, University of Ulsan, 93 Daehak-ro, Nam-gu, Ulsan, 44610 Republic of Korea
| | - Yung-Keun Kwon
- Department of Electrical/Electronic and Computer Engineering, University of Ulsan, 93 Daehak-ro, Nam-gu, Ulsan, 44610 Republic of Korea
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17
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Zhao H, Zhang G, Pang L, Lan Y, Wang L, Yu F, Hu J, Li F, Zhao T, Xiao Y, Li X. ‘Traffic light rules’: Chromatin states direct miRNA-mediated network motifs running by integrating epigenome and regulatome. Biochim Biophys Acta Gen Subj 2016; 1860:1475-88. [DOI: 10.1016/j.bbagen.2016.04.008] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2015] [Revised: 03/11/2016] [Accepted: 04/13/2016] [Indexed: 12/14/2022]
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